The multicore era has emerged the need for highly parallel software. Since automatic parallelization has resulted ineffective in many production codes, the community hopes for the development of tools that may assist parallelization, providing hints to drive the parallelization process. In our previous work, we had designed Tareador, a tool based on dynamic instrumentation that identifies potential task-based parallelism inherent in applications. Also, we showed how a programmer can use Tareador to explore the potential of different parallelization strategies. In this paper, we build up on our previous work by automating the process of exploring parallelism. We have designed an environment that, given a sequential code and configuration of the target parallel architecture, iteratively runs Tareador to find an efficient parallelization strategy. We propose an autonomous algorithm based on simple metrics and a cost function. The algorithm finds an efficient parallelization strategy and provides the programmer with sufficient information to turn that parallelization strategy into an actual parallel program.